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Forecasting Financial Crashes: Revisit to Log-Periodic Power Law
Joint Authors
Dai, Bingcun
Zhang, Fan
Tarzia, Domenico
Ahn, Kwangwon
Source
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-08-01
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
We aim to provide an algorithm to predict the distribution of the critical times of financial bubbles employing a log-periodic power law.
Our approach consists of a constrained genetic algorithm and an improved price gyration method, which generates an initial population of parameters using historical data for the genetic algorithm.
The key enhancements of price gyration algorithm are (i) different window sizes for peak detection and (ii) a distance-based weighting approach for peak selection.
Our results show a significant improvement in the prediction of financial crashes.
The diagnostic analysis further demonstrates the accuracy, efficiency, and stability of our predictions.
American Psychological Association (APA)
Dai, Bingcun& Zhang, Fan& Tarzia, Domenico& Ahn, Kwangwon. 2018. Forecasting Financial Crashes: Revisit to Log-Periodic Power Law. Complexity،Vol. 2018, no. 2018, pp.1-12.
https://search.emarefa.net/detail/BIM-1134051
Modern Language Association (MLA)
Dai, Bingcun…[et al.]. Forecasting Financial Crashes: Revisit to Log-Periodic Power Law. Complexity No. 2018 (2018), pp.1-12.
https://search.emarefa.net/detail/BIM-1134051
American Medical Association (AMA)
Dai, Bingcun& Zhang, Fan& Tarzia, Domenico& Ahn, Kwangwon. Forecasting Financial Crashes: Revisit to Log-Periodic Power Law. Complexity. 2018. Vol. 2018, no. 2018, pp.1-12.
https://search.emarefa.net/detail/BIM-1134051
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1134051